Processing and cross reference of realtime natural language dialog for live annotations

ABSTRACT

An approach is provided to receive audible speech and convert the received speech to text while the audible speech is being delivered to a user. An annotation candidate is identified in the text and an annotation reference relating to the identified annotation candidate is retrieved and presented to the user.

BACKGROUND

In a live forum such as a conference or panel discussion, speakers maymake reference to publications, laws, and other outside material that isnot readily available. A listener may want more information regardingthe item cited. Today the user would have to manually make note of whatwas said and return to it later, or at best be searching on the internetfor it while also trying to follow the rest of the discussion. This is aclumsy solution that splits the listener's attention, increasing thelikelihood that they miss something important, write down the wrongthing, or forget to follow up on it later.

BRIEF SUMMARY

According to one embodiment of the present disclosure, an approach isprovided to receive audible speech and convert the received speech totext while the audible speech is being delivered to a user. Anannotation candidate is identified in the text and an annotationreference relating to the identified annotation candidate is retrievedand presented to the user.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosure,as defined solely by the claims, will become apparent in thenon-limiting detailed description set forth below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 illustrates an information handling system, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein;

FIG. 3 is an exemplary diagram depicting the system that processes andcross references of realtime natural language dialog for liveannotations;

FIG. 4 is an exemplary set of flowcharts to process live annotationinputs;

FIG. 5 is an exemplary flowchart that filters live annotationcandidates; and

FIG. 6 is an exemplary flowchart that presents a live annotationcandidate to users.

DETAILED DESCRIPTION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. The following detailed description willgenerally follow the summary of the disclosure, as set forth above,further explaining and expanding the definitions of the various aspectsand embodiments of the disclosure as necessary.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 in a computer network 102. QA system 100may include knowledge manager 104, which comprises one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like. Computer network 102may include other computing devices in communication with each other andwith other devices or components via one or more wired and/or wirelessdata communication links, where each communication link may comprise oneor more of wires, routers, switches, transmitters, receivers, or thelike. QA system 100 and network 102 may enable question/answer (QA)generation functionality for one or more content users. Otherembodiments may include QA system 100 interacting with components,systems, sub-systems, and/or devices other than those depicted herein.

QA system 100 may receive inputs from various sources. For example, QAsystem 100 may receive input from the network 102, a corpus ofelectronic documents 107 or other data, semantic data 108, and otherpossible sources of input. In one embodiment, some or all of the inputsto QA system 100 route through the network 102 and stored in knowledgebase 106. The various computing devices on the network 102 may includeaccess points for content creators and content users. Some of thecomputing devices may include devices for a database storing the corpusof data. The network 102 may include local network connections andremote connections in various embodiments, such that QA system 100 mayoperate in environments of any size, including local and global, e.g.,the Internet. Additionally, QA system 100 serves as a front-end systemthat can make available a variety of knowledge extracted from orrepresented in documents, network-accessible sources and/or structureddata sources. In this manner, some processes populate the knowledgemanager with the knowledge manager also including input interfaces toreceive knowledge requests and respond accordingly.

In one embodiment, a content creator creates content in a document 107for use as part of a corpus of data with QA system 100. The document 107may include any file, text, article, or source of data for use in QAsystem 100. Content users may access QA system 100 via a networkconnection or an Internet connection to the network 102, and may inputquestions to QA system 100, which QA system 100 answers according to thecontent in the corpus of data. As further described below, when aprocess evaluates a given section of a document for semantic content,the process can use a variety of conventions to query it from knowledgemanager 104. One convention is to send a well-formed question.

Semantic data 108 is content based on the relation between signifiers,such as words, phrases, signs, and symbols, and what they stand for,their denotation, or connotation. In other words, semantic data 108 iscontent that interprets an expression, such as by using Natural LanguageProcessing (NLP). In one embodiment, the process sends well-formedquestions (e.g., natural language questions, etc.) to QA system 100 andQA system 100 may interpret the question and provide a response thatincludes one or more answers to the question. In some embodiments, QAsystem 100 may provide a response to users in a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems shown in FIG. 1 depicts separate nonvolatile data stores (server160 utilizes nonvolatile data store 165, and mainframe computer 170utilizes nonvolatile data store 175. The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 235 to Trusted Platform Module (TPM) 295.Other components often included in Southbridge 235 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 235to nonvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 0.802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 200 and another computer system or device.Optical storage device 290 connects to Southbridge 235 using Serial ATA(SATA) bus 288. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 235to other forms of storage devices, such as hard disk drives. Audiocircuitry 260, such as a sound card, connects to Southbridge 235 via bus258. Audio circuitry 260 also provides functionality such as audioline-in and optical digital audio in port 262, optical digital outputand headphone jack 264, internal speakers 266, and internal microphone268. Ethernet controller 270 connects to Southbridge 235 using a bus,such as the PCI or PCI Express bus. Ethernet controller 270 connectsinformation handling system 200 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIGS. 3-6 depict an approach that analyzes a verbal presentation, suchas a speech, in real time for references to outside assets. A search isthen done in realtime for the asset in question and a link to that assetis returned in an unobtrusive way to the listener who can then follow upon it at their leisure. The system is comprised of the following pieces:(1) a method for speech recognition; (2) a method of natural languageanalysis that identifies references to outside assets (legal documents,court cases, publications, or other media); (3) a method of retrieving alink to the asset; and (4) a method of presenting that link in anunobtrusive way to the entire audience.

The system uses some method of speech recognition and translates it intoa string of text. The system then parses each string of text looking forparts of speech that represent known entities that can be found throughsearching. Known entities can be defined using several methodsincluding, but not limited to, any of the following: (1) RegularExpression (regex) matching; (2) Lexical Types; and (3) Parts of Speech.Examples of entities the system could look for include references to lawdocuments, court cases, published works and other media.

When an entity is identified, the system then retrieves a link to thereferenced item. For example when a law is referenced, A link to thecontent of the section of law referenced will be retrieved. The link canpoint to a locally stored resource, a remote resource such as a URL, orany other type of resource available to the system. That link is thenpresented to the user in a way which is unobtrusive to the user, so asnot to distract their attention from the live event, but that the userwill acknowledge and understand is relevant to something that was justsaid. For example the system may provide an electronic alert that showsthe reference quoted along with a link or icon that the user recognizesas a link.

By way of an example, during the presidential debates of 2012, candidateMitt Romney stated “Russia's said they're not going to follow Nunn-Lugaranymore; they'll back away from their nuclear proliferation treaty thatwe had with them.” Here, the system could identify the term “Nunn-Lugar”as a searchable entity, search the term “Nunn-Lugar,” and return ahyperlink to the user that, when selected by the user such as using asmart phone, retrieves information related to the Nunn-Lugar nuclearproliferation treaty.

The approach presented herein can use several methods to identifyreferences to an outside asset. One example could be a combination ofmetrics based on the following factors: (1) Rarity of the word in thecorpus (“Nunn-Lugar”) The word Nunn-Lugar would be a known word in acorpus based on an online encyclopedia, but would be relatively rare;(2) Phrase matches such as “Smith versus Maryland.” Simple regexmatching of phrases from the dialog that are known to be references, forexample a court case. (3) Contextual hints (e.g., “according to section215 of . . . ”, etc.). Phrases like “according to” or “as seen in” are aclue that the subject is a reference, especially when combined withregex phrase matching.

After identifying the reference, the system can use filters to determinewhether to actually retrieve data pertaining to the reference based on anumber of criteria. First, “Commonality” can be used as a filter (e.g.,“1st Amendment,” etc.). If the reference refers to something commonlyknown by the public, the system may choose to filter it out. For examplemost people know what the 1st Amendment refers to, and would not needadditional explanation. What counts as common could be based onsomething like rarity in the corpus as mentioned above, or commonalitycould be learned by the system over time. Second, Quantity, signal vs.noise could be used as a filter. The threshold could be variable so asto not allow more than a particular number (“x”) references to bepresented in a given amount of time.

FIG. 3 is an exemplary diagram depicting the system that processes andcross references of realtime natural language dialog for liveannotations. Speaker 300 is delivering audible speech 310 to a liveaudience 320. Users of the system are members of the live audience. Thelive audience may be at the same venue as speaker 300 or may bereceiving the audible speech live via a radio broadcast, televisionbroadcast, web broadcast, or other multimedia type delivery system.

Live annotation processor 330 converts the audible speech to speech textand identifies annotation candidates using the speech text. In oneembodiment, the identification of annotation candidates is performedusing QA system 100. Annotation data 350 includes annotation referencesthat are presented to users included in live audience 320 via annotationpresentation 360. Annotation presentation 360 may be a link to theannotation reference or may be descriptive text pertaining to theannotation candidate. For example, if speaker 300 refers to an uncommonor otherwise obscure term, the live annotation processor would identifythat the term is uncommon, identify annotation data referring to theterm, and present the annotation data, such as a link or a descriptivetext, to users included in live audience 320.

FIG. 4 is an exemplary set of flowcharts to process live annotationinputs. FIG. 4 processing commences at 400 and shows the steps taken bya process that performs a routine that processes live annotation input.At step 405, the process receives and buffers audible speech that isbeing delivered by a speaker. The buffered speech is stored in datastore 410. At step 415, the process retrieves a segment of audiblespeech from data store 410. At step 420, the process translates thespeech segment to text and stores the text in data store 425. Theprocess determines as to whether there is more speech to process(decision 430). If there is more speech to process, then decision 430branches to the ‘yes’ branch which loops back to receive and process thenext audible speech from the speaker. This looping continues until thereis no more speech to process, at which point decision 430 branches tothe ‘no’ branch and live annotation input processing ends at 435.

Concurrent with the live annotation input process, the live annotationprocess processes the speech text to identify annotation candidates andannotation references that are delivered to users. The live annotationprocess commences at 400 whereupon, at step 445, the process retrievesthe first segment of speech text from data store 425. The retrieved textsegment (e.g., sentence, phrase, etc.) is stored in memory area 450. Atpredefined process 455, the process performs the Annotation CandidateFilter routine (see FIG. 5 and corresponding text for processingdetails). Predefined process 455 processes the text segment stored inmemory area 450 and identifies annotation candidates and scorespertaining to such candidates. The process determines as to whether theannotation candidate score exceeds a given threshold (decision 465). Ifthe annotation candidate score exceeds the threshold, then decision 465branches to the ‘yes’ branch whereupon, at predefined process 470, theprocess performs the Present Annotation Candidate routine (see FIG. 6and corresponding text for processing details).

On the other hand, if the annotation candidate score does not exceed thethreshold, then decision 465 branches to the ‘no’ branch bypassingpredefined process 470. The process determines as to whether there ismore speech text to process (decision 480). If there is more speech textto process, then decision 480 branches to the ‘yes’ branch which loopsback to step 445 to retrieve and process the next segment of speech textas described above. This looping continues until there is more speechtext to process, at which point decision 480 branches to the ‘no’ branchand processing ends at 490.

FIG. 5 is an exemplary flowchart that filters live annotationcandidates. FIG. 5 processing commences at 500 and shows the steps takenby a process that performs a routine that filters annotation candidates.At step 510, the process retrieves the text segment from memory area 450and parses the text segment into parts of speech such as objects, nouns,verbs, subjects, and the like. These parts of speech are stored inmemory area 520. At step 525, the process selects the first part of thesegment from memory area 520.

At step 530, the process checks if the selected part of speechrepresents a locatable entity using Regular Expression (RegEx) matching,Lexical type matching, and parts of speech analysis. This check can befacilitated using corpus 106 included in a question answering (QA)system. The process determines as to whether the selected partrepresents a locatable entity (decision 540). If the selected partrepresents a locatable entity, then decision 540 branches to the ‘yes’branch for further processing. On the other hand, if the selected partdoes not represent a locatable entity, then decision 540 branches to the‘no’ branch bypassing the remaining steps.

At step 550, the process identifies the commonality of the entity andprovide commonality score (e.g., “1st amendment,” “Obama,” etc.). Theprocess determines as to whether the entity is a commonly known entity(decision 560). Data pertaining to commonly known entities is notparticularly useful to augment the user with additional data during thelive presentation. Therefore, if the entity is commonly known, thendecision 560 branches to the ‘yes’ branch bypassing the remaining steps.On the other hand, if the entity is not commonly known, then decision560 branches to the ‘no’ branch to perform steps 570 through 585.

At step 570, the process checks the rarity, or “known-ness,” of theentity and computes a rarity score. Entities that are rare receive ahigher score than entities that are not rare. At step 575, the processchecks for matching phrases that indicate that the entity is aninteresting term (e.g., court cases, etc.) and the process computes amatching phrase score. Terms that relate to a matching phrase such as “alandmark decision,” “court case,” “ruling,” etc. receive a highermatching phrase score than terms that are not part of a matching phrase.At step 580, the process identifies whether the entity is signaled by aparticular context (e.g., “according to,” “as seen in,” etc.) and theprocess computes a signal strength score. An entity signaled by aparticular context receives a higher signal strength score than anentity that is not signaled by a particular context. At step 585, theprocess combines the rarity score, the matching phrase score, and thesignal strength score and returns the entity and its overall score inmemory area 460.

The process determines as to whether there are more parts to process(decision 590). If there are more parts to process, then decision 590branches to the ‘yes’ branch which loops back to select and process thenext part of the text segment as described above. This looping continuesuntil there are no more parts to process from text segment 450, at whichpoint decision 590 branches to the ‘no’ branch and processing returns tothe calling routine (see FIG. 4) at 595. Any identified entities(annotation candidates) and their respective scores are returned to thecalling routine via memory area 460.

FIG. 6 is an exemplary flowchart that presents a live annotationcandidate to users. FIG. 6 processing commences at 600 and shows thesteps taken by a process that performs a routine that presents anannotation candidate to the user. At step 610, the process reads theuser's presentation preferences from data store 620. The processdetermines as to whether a quantity, signal vs. noise preference hasbeen activated based on the user's preferences (decision 625). If thequantity, signal vs. noise preference has been activated, then decision625 branches to the ‘yes’ branch to process the preference. On the otherhand, this preference has not been activated, then decision 625 branchesto the ‘no’ branch bypassing decisions 630 and 640.

If the preference is activated, the process determines as to whether toomany entities have been presented during given amount of time (decision630). If too many entities have been presented during given amount oftime, then decision 630 branches to the ‘yes’ branch for exceptionchecking. On the other hand, too many entities have not been presentedduring given amount of time, then decision 630 branches to the ‘no’branch bypassing decision 640.

The process determines as to whether to allow this annotation candidateas an exception due to a high entity score (decision 640). If thedetermination is to allow this annotation candidate as an exception dueto a high entity score, then decision 640 branches to the ‘yes’ branchto present the annotation candidate. On the other hand, if thedetermination is not to allow this annotation candidate as an exceptiondue to a high entity score, then decision 640 branches to the ‘no’branch whereupon, at step 690, the system refrains from presenting theannotation candidate to the user.

If the quantity, signal vs. noise preference is not activated (withdecision 625 branching to the ‘no’ branch), or there have not been toomany entities presented during a given period of time (with decision 630branching to the ‘no’ branch), or the annotation candidate is beingallowed for presentation due to a high entity score (with decision 640branching to the ‘yes’ branch), then, at step 650, the process retrievesthe annotation reference (link and/or data) that describes theannotation candidate. The process determines as to whether the user'sdelivery preference is to provide a link (e.g., hyperlink, etc.) or toprovide descriptive text to the user (decision 660). If the deliverypreference is to provide a link, then decision 660 branches to the‘link’ branch whereupon, at step 670 the process sends link toindividual audience members (users) via the individual users' preferredmethods (e.g., smart phone, etc.). On the other hand, if the deliverypreference is to provide descriptive text, then decision 660 branches tothe ‘text’ branch whereupon, at step 680 the process sends descriptivetext to the individual audience members (users) via the individualusers' preferred methods (e.g., text message, etc.). FIG. 6 processingthereafter returns to the calling routine (see FIG. 4) at 695.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. Furthermore, it is to be understood that thedisclosure is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to disclosures containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

The invention claimed is:
 1. A method implemented by an information handling system that includes a memory and a processor, the method comprising: converting audible speech received by a microphone to text, wherein the audible speech is being delivered to a user; identifying an annotation candidate included in the text; combining a rarity score pertaining to the identified annotation candidate, a phrase matching score pertaining to the identified annotation candidate, and a signal strength score pertaining to a context of the identified annotation candidate into an overall score; in response to the overall score exceeding a threshold, retrieving an annotation reference relating to the identified annotation candidate; and presenting the annotation reference to the user.
 2. The method of claim 1 wherein the annotation reference is presented to the user while the audible speech is still being delivered.
 3. The method of claim 1 further comprising: parsing the text into a plurality of parts; identifying a locatable entity as one of the parts; and filtering the locatable entity, wherein the filtering results in the annotation candidate.
 4. The method of claim 3 wherein the filtering further comprises: identifying that the locatable entity is an uncommon entity.
 5. The method of claim 4 further comprising: generating the rarity score pertaining to the uncommon entity; generating the phrase matching score pertaining to the uncommon entity; and generating the signal strength score pertaining to a context of the uncommon entity.
 6. The method of claim 1 further comprising: determining whether to present the annotation reference based on a number of a plurality of annotation references previously presented to the user in a given amount of time; presenting the annotation reference in response to the number of the plurality of annotation references not exceeding a threshold; and refraining from presenting the annotation reference in response to the number of the plurality of annotation references exceeding the threshold.
 7. The method of claim 1 further comprising: retrieving a delivery preference pertaining to the user, wherein the delivery preference is selected from the group consisting of a link and a text; presenting an annotation link to the annotation reference in response to the delivery preference being the link; and presenting a descriptive text of the annotation reference in response to the delivery preference being the text.
 8. An information handling system comprising: one or more processors; one or more data stores accessible by at least one of the processors; a microphone; a memory coupled to at least one of the processors; and a set of computer program instructions stored in the memory and executed by at least one of the processors in order to perform actions of: converting audible speech received by the microphone to text, wherein the audible speech is being delivered to a user; identifying an annotation candidate included in the text; combining a rarity score pertaining to the identified annotation candidate, a phrase matching score pertaining to the identified annotation candidate, and a signal strength score pertaining to a context of the identified annotation candidate into an overall score; in response to the overall score exceeding a threshold, retrieving an annotation reference relating to the identified annotation candidate; and presenting the annotation reference to the user.
 9. The information handling system of claim 8 wherein the annotation reference is presented to the user while the audible speech is still being delivered.
 10. The information handling system of claim 8 wherein the actions further comprise: parsing the text into a plurality of parts; identifying a locatable entity as one of the parts; and filtering the locatable entity, wherein the filtering results in the annotation candidate.
 11. The information handling system of claim 10 wherein the filtering further comprises: identifying that the locatable entity is an uncommon entity.
 12. The information handling system of claim 11 wherein the actions further comprise: generating the rarity score pertaining to the uncommon entity; generating the phrase matching score pertaining to the uncommon entity; and generating the signal strength score pertaining to a context of the uncommon entity.
 13. The information handling system of claim 8 wherein the actions further comprise: determining whether to present the annotation reference based on a number of a plurality of annotation references previously presented to the user in a given amount of time; presenting the annotation reference in response to the number of the plurality of annotation references not exceeding a threshold; and refraining from presenting the annotation reference in response to the number of the plurality of annotation references exceeding the threshold.
 14. The information handling system of claim 8 wherein the actions further comprise: retrieving a delivery preference pertaining to the user, wherein the delivery preference is selected from the group consisting of a link and a text; presenting an annotation link to the annotation reference in response to the delivery preference being the link; and presenting a descriptive text of the annotation reference in response to the delivery preference being the text.
 15. A computer program product stored in a non-transitory computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: converting audible speech received by a microphone to text, wherein the audible speech is being delivered to a user; identifying an annotation candidate included in the text; combining a rarity score pertaining to the identified annotation candidate, a phrase matching score pertaining to the identified annotation candidate, and a signal strength score pertaining to a context of the identified annotation candidate into an overall score; in response to the overall score exceeding a threshold, retrieving an annotation reference relating to the identified annotation candidate; and presenting the annotation reference to the user.
 16. The computer program product of claim 15 wherein the annotation reference is presented to the user while the audible speech is still being delivered.
 17. The computer program product of claim 15 wherein the actions further comprise: parsing the text into a plurality of parts; identifying a locatable entity as one of the parts; and filtering the locatable entity, wherein the filtering results in the annotation candidate.
 18. The computer program product of claim 17 wherein the filtering further comprises: identifying that the locatable entity is an uncommon entity.
 19. The computer program product of claim 18 wherein the actions further comprise: generating the rarity score pertaining to the uncommon entity; generating the phrase matching score pertaining to the uncommon entity; and generating the signal strength score pertaining to a context of the uncommon entity.
 20. The computer program product of claim 15 wherein the actions further comprise: determining whether to present the annotation reference based on a number of a plurality of annotation references previously presented to the user in a given amount of time; presenting the annotation reference in response to the number of the plurality of annotation references not exceeding a threshold; and refraining from presenting the annotation reference in response to the number of the plurality of annotation references exceeding the threshold. 